Probabilistic techniques & algorithms

April 6-7, 2012

Austin, TX

Overview

The design of fast algorithms plays a crucial role in various branches of applied mathematics and engineering. Often an algorithm's efficiency can be greatly improved using randomization, at the cost of allowing for a small probability of failure. Examples include the use of random measurements in compressed sensing and randomized algorithms for fast linear algebra. Probabilistic techniques can also be of use when the complexity of the data prevents the efficient use of adaptive algorithms. An example is given by nearest neighbor searches in massive high-dimensional data sets. Applying a random projection can make the calculations more efficient while, with high probability, introducing only a small error.

The workshop aims to bring together experts from different fields in applied mathematics, computer science and engineering whose research involves probabilistic techniques and randomized algorithms. The goal is to put recent results into perspective, encourage collaboration, and to discuss how the probabilistic viewpoint can find new fields of application.

Limited funding for local support will be provided for young participants. Funding decisions have been made.